Integrates get_genotype in paddleslim (PaddlePaddle#228)

demo/darts/genotypes.py

@@ -21,62 +21,6 @@
     'sep_conv_5x5', 'dil_conv_3x3', 'dil_conv_5x5'
 ]
 
-NASNet = Genotype(
-    normal=[
-        ('sep_conv_5x5', 1),
-        ('sep_conv_3x3', 0),
-        ('sep_conv_5x5', 0),
-        ('sep_conv_3x3', 0),
-        ('avg_pool_3x3', 1),
-        ('skip_connect', 0),
-        ('avg_pool_3x3', 0),
-        ('avg_pool_3x3', 0),
-        ('sep_conv_3x3', 1),
-        ('skip_connect', 1),
-    ],
-    normal_concat=[2, 3, 4, 5, 6],
-    reduce=[
-        ('sep_conv_5x5', 1),
-        ('sep_conv_7x7', 0),
-        ('max_pool_3x3', 1),
-        ('sep_conv_7x7', 0),
-        ('avg_pool_3x3', 1),
-        ('sep_conv_5x5', 0),
-        ('skip_connect', 3),
-        ('avg_pool_3x3', 2),
-        ('sep_conv_3x3', 2),
-        ('max_pool_3x3', 1),
-    ],
-    reduce_concat=[4, 5, 6], )
-
-AmoebaNet = Genotype(
-    normal=[
-        ('avg_pool_3x3', 0),
-        ('max_pool_3x3', 1),
-        ('sep_conv_3x3', 0),
-        ('sep_conv_5x5', 2),
-        ('sep_conv_3x3', 0),
-        ('avg_pool_3x3', 3),
-        ('sep_conv_3x3', 1),
-        ('skip_connect', 1),
-        ('skip_connect', 0),
-        ('avg_pool_3x3', 1),
-    ],
-    normal_concat=[4, 5, 6],
-    reduce=[
-        ('avg_pool_3x3', 0),
-        ('sep_conv_3x3', 1),
-        ('max_pool_3x3', 0),
-        ('sep_conv_7x7', 2),
-        ('sep_conv_7x7', 0),
-        ('avg_pool_3x3', 1),
-        ('max_pool_3x3', 0),
-        ('max_pool_3x3', 1),
-        ('conv_7x1_1x7', 0),
-        ('sep_conv_3x3', 5),
-    ],
-    reduce_concat=[3, 4, 6])
-
 DARTS_V1 = Genotype(
     normal=[('sep_conv_5x5', 0), ('dil_conv_3x3', 1), ('sep_conv_3x3', 2),
             ('sep_conv_5x5', 0), ('sep_conv_5x5', 0), ('dil_conv_3x3', 3),

demo/darts/model_search.py

@@ -22,7 +22,6 @@
 from paddle.fluid.dygraph.nn import Conv2D, Pool2D, BatchNorm, Linear
 from paddle.fluid.dygraph.base import to_variable
 from genotypes import PRIMITIVES
-from genotypes import Genotype
 from operations import *
 
 
@@ -147,6 +146,7 @@ def __init__(self,
         self._layers = layers
         self._steps = steps
         self._multiplier = multiplier
+        self._primitives = PRIMITIVES
         self._method = method
 
         c_cur = stem_multiplier * c_in
@@ -238,7 +238,7 @@ def new(self):
 
     def _initialize_alphas(self):
         k = sum(1 for i in range(self._steps) for n in range(2 + i))
-        num_ops = len(PRIMITIVES)
+        num_ops = len(self._primitives)
         self.alphas_normal = fluid.layers.create_parameter(
             shape=[k, num_ops],
             dtype="float32",
@@ -268,58 +268,3 @@ def _initialize_alphas(self):
 
     def arch_parameters(self):
         return self._arch_parameters
-
-    def genotype(self):
-        def _parse(weights, weights2=None):
-            gene = []
-            n = 2
-            start = 0
-            for i in range(self._steps):
-                end = start + n
-                W = weights[start:end].copy()
-                if self._method == "PC-DARTS":
-                    W2 = weights2[start:end].copy()
-                    for j in range(n):
-                        W[j, :] = W[j, :] * W2[j]
-                edges = sorted(range(i + 2), key=lambda x: -max(W[x][k] for k in range(len(W[x])) if k != PRIMITIVES.index('none')))[:2]
-                for j in edges:
-                    k_best = None
-                    for k in range(len(W[j])):
-                        if k != PRIMITIVES.index('none'):
-                            if k_best is None or W[j][k] > W[j][k_best]:
-                                k_best = k
-                    gene.append((PRIMITIVES[k_best], j))
-                start = end
-                n += 1
-            return gene
-
-        weightsr2 = None
-        weightsn2 = None
-        if self._method == "PC-DARTS":
-            n = 3
-            start = 2
-            weightsr2 = fluid.layers.softmax(self.betas_reduce[0:2])
-            weightsn2 = fluid.layers.softmax(self.betas_normal[0:2])
-            for i in range(self._steps - 1):
-                end = start + n
-                tw2 = fluid.layers.softmax(self.betas_reduce[start:end])
-                tn2 = fluid.layers.softmax(self.betas_normal[start:end])
-                start = end
-                n += 1
-                weightsr2 = fluid.layers.concat([weightsr2, tw2])
-                weightsn2 = fluid.layers.concat([weightsn2, tn2])
-            weightsr2 = weightsr2.numpy()
-            weightsn2 = weightsn2.numpy()
-
-        gene_normal = _parse(
-            fluid.layers.softmax(self.alphas_normal).numpy(), weightsn2)
-        gene_reduce = _parse(
-            fluid.layers.softmax(self.alphas_reduce).numpy(), weightsr2)
-
-        concat = range(2 + self._steps - self._multiplier, self._steps + 2)
-        genotype = Genotype(
-            normal=gene_normal,
-            normal_concat=concat,
-            reduce=gene_reduce,
-            reduce_concat=concat)
-        return genotype

paddleslim/nas/darts/get_genotype.py

@@ -0,0 +1,78 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import paddle.fluid as fluid
+from collections import namedtuple
+
+Genotype = namedtuple('Genotype', 'normal normal_concat reduce reduce_concat')
+
+
+def get_genotype(model):
+    def _parse(weights, weights2=None):
+        gene = []
+        n = 2
+        start = 0
+        for i in range(model._steps):
+            end = start + n
+            W = weights[start:end].copy()
+            if model._method == "PC-DARTS":
+                W2 = weights2[start:end].copy()
+                for j in range(n):
+                    W[j, :] = W[j, :] * W2[j]
+            edges = sorted(range(i + 2), key=lambda x: -max(W[x][k] for k in range(len(W[x])) if k != model._primitives.index('none')))[:2]
+            for j in edges:
+                k_best = None
+                for k in range(len(W[j])):
+                    if k != model._primitives.index('none'):
+                        if k_best is None or W[j][k] > W[j][k_best]:
+                            k_best = k
+                gene.append((model._primitives[k_best], j))
+            start = end
+            n += 1
+        return gene
+
+    weightsr2 = None
+    weightsn2 = None
+    if model._method == "PC-DARTS":
+        n = 3
+        start = 2
+        weightsr2 = fluid.layers.softmax(model.betas_reduce[0:2])
+        weightsn2 = fluid.layers.softmax(model.betas_normal[0:2])
+        for i in range(model._steps - 1):
+            end = start + n
+            tw2 = fluid.layers.softmax(model.betas_reduce[start:end])
+            tn2 = fluid.layers.softmax(model.betas_normal[start:end])
+            start = end
+            n += 1
+            weightsr2 = fluid.layers.concat([weightsr2, tw2])
+            weightsn2 = fluid.layers.concat([weightsn2, tn2])
+        weightsr2 = weightsr2.numpy()
+        weightsn2 = weightsn2.numpy()
+
+    gene_normal = _parse(
+        fluid.layers.softmax(model.alphas_normal).numpy(), weightsn2)
+    gene_reduce = _parse(
+        fluid.layers.softmax(model.alphas_reduce).numpy(), weightsr2)
+
+    concat = range(2 + model._steps - model._multiplier, model._steps + 2)
+    genotype = Genotype(
+        normal=gene_normal,
+        normal_concat=concat,
+        reduce=gene_reduce,
+        reduce_concat=concat)
+    return genotype

paddleslim/nas/darts/train_search.py

@@ -24,6 +24,7 @@
 from paddle.fluid.dygraph.base import to_variable
 from ...common import AvgrageMeter, get_logger
 from .architect import Architect
+from .get_genotype import get_genotype
 logger = get_logger(__name__, level=logging.INFO)
 
 
@@ -201,7 +202,7 @@ def train(self):
         for epoch in range(self.num_epochs):
             logger.info('Epoch {}, lr {:.6f}'.format(
                 epoch, optimizer.current_step_lr()))
-            genotype = self.model.genotype()
+            genotype = get_genotype(self.model)
             logger.info('genotype = %s', genotype)
 
             train_top1 = self.train_one_epoch(train_loader, valid_loader,